---------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  E:\PHD\Research\EITC public opinion\EITC HQ JH\Data\Replication data\CCES_analyses.log
  log type:  text
 opened on:  25 Sep 2023, 12:32:48

. //program:  CCES_analyses.do
. //task:     Replication code for CCES analyses
. //project:  Ideology, Information, and Social Welfare Preferences (American Politics Research)   
. //authors: Hang Qi & Jake Haselswerdt
. //updated 9/19/23
. 
. //program setup
. version 18

. clear all

. set linesize 80

. macro drop _all

. set scheme s1mono

. set more off

. 
. ******************************************************************************
> ******
. *** Part 1. Data Analysis
. ******************************************************************************
> ******
. //chekcing variables and unconditional experimental results
. use CCES.dta, clear
(CCES 2020 module data cleaned for Qi & Haselswerdt policy info experiment, 2021
> -)

. **global control variables
. global control "pid7_clean age_ordinal White educ female faminc_clean"

. 
. **checking variables
. *variables of interest
. codebook eitc_support tanf_support eitc_ineq eitc_admin tanf_ineq tanf_admin i
> deology liberal conservative moderate polknow

--------------------------------------------------------------------------------
eitc_support                                                 EITC policy support
--------------------------------------------------------------------------------

                  Type: Numeric (float)
                 Label: support

                 Range: [1,5]                         Units: 1
         Unique values: 5                         Missing .: 4/1,001

            Tabulation: Freq.   Numeric  Label
                           81         1  Oppose strongly
                          123         2  Oppose somewhat
                          341         3  Neither support nor oppose
                          280         4  Support somewhat
                          172         5  Strongly support
                            4         .  

--------------------------------------------------------------------------------
tanf_support                                                 TANF policy support
--------------------------------------------------------------------------------

                  Type: Numeric (float)
                 Label: support

                 Range: [1,5]                         Units: 1
         Unique values: 5                         Missing .: 4/1,001

            Tabulation: Freq.   Numeric  Label
                          102         1  Oppose strongly
                          178         2  Oppose somewhat
                          337         3  Neither support nor oppose
                          231         4  Support somewhat
                          149         5  Strongly support
                            4         .  

--------------------------------------------------------------------------------
eitc_ineq                                              EITC inadequacy treatment
--------------------------------------------------------------------------------

                  Type: Numeric (float)
                 Label: eitc_ineq

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          503         0  EITC fraud treatment
                          497         1  EITC inadequacy treatment
                            1         .  

--------------------------------------------------------------------------------
eitc_admin                                                  EITC fraud treatment
--------------------------------------------------------------------------------

                  Type: Numeric (float)
                 Label: eitc_admin

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          497         0  EITC inadequacy treatment
                          503         1  EITC fraud treatment
                            1         .  

--------------------------------------------------------------------------------
tanf_ineq                                              TANF inadequacy treatment
--------------------------------------------------------------------------------

                  Type: Numeric (float)
                 Label: tanf_ineq

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          497         0  TANF fraud treatment
                          503         1  TANF inadequacy treatment
                            1         .  

--------------------------------------------------------------------------------
tanf_admin                                                  TANF fraud treatment
--------------------------------------------------------------------------------

                  Type: Numeric (float)
                 Label: tanf_admin

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          503         0  TANF inadequacy treatment
                          497         1  TANF fraud treatment
                            1         .  

--------------------------------------------------------------------------------
ideology                                                                Ideology
--------------------------------------------------------------------------------

                  Type: Numeric (byte)
                 Label: ideo5

                 Range: [1,5]                         Units: 1
         Unique values: 5                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          120         1  Very liberal
                          180         2  Liberal
                          412         3  Moderate
                          175         4  Conservative
                          113         5  Very conservative
                            1         .  

--------------------------------------------------------------------------------
liberal                                                  Liberal or very liberal
--------------------------------------------------------------------------------

                  Type: Numeric (float)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.  Value
                          700  0
                          300  1
                            1  .

--------------------------------------------------------------------------------
conservative                                   Conservative or very conservative
--------------------------------------------------------------------------------

                  Type: Numeric (float)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.  Value
                          712  0
                          288  1
                            1  .

--------------------------------------------------------------------------------
moderate                                                                Moderate
--------------------------------------------------------------------------------

                  Type: Numeric (float)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.  Value
                          667  0
                          333  1
                            1  .

--------------------------------------------------------------------------------
polknow                                                      Political knowledge
--------------------------------------------------------------------------------

                  Type: Numeric (float)

                 Range: [0,8]                         Units: 1
         Unique values: 9                         Missing .: 1/1,001

            Tabulation: Freq.  Value
                           98  0
                           45  1
                           55  2
                           68  3
                           73  4
                           79  5
                          127  6
                          104  7
                          351  8
                            1  .

. 
. *controls
. codebook $control

--------------------------------------------------------------------------------
pid7_clean                                                      7 point Party ID
--------------------------------------------------------------------------------

                  Type: Numeric (byte)
                 Label: pid7

                 Range: [1,7]                         Units: 1
         Unique values: 7                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          259         1  Strong Democrat
                          120         2  Not very strong Democrat
                          107         3  Lean Democrat
                          170         4  Independent
                           88         5  Lean Republican
                           82         6  Not very strong Republican
                          174         7  Strong Republican
                            1         .  

--------------------------------------------------------------------------------
age_ordinal                                               Age (6-category scale)
--------------------------------------------------------------------------------

                  Type: Numeric (float)
                 Label: age

                 Range: [1,6]                         Units: 1
         Unique values: 6                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          197         1  18-29
                          158         2  30-39
                          159         3  40-49
                          172         4  50-59
                          120         5  60-65
                          194         6  65+
                            1         .  

--------------------------------------------------------------------------------
White                                                                      White
--------------------------------------------------------------------------------

                  Type: Numeric (float)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.  Value
                          279  0
                          721  1
                            1  .

--------------------------------------------------------------------------------
educ                                                                   Education
--------------------------------------------------------------------------------

                  Type: Numeric (byte)
                 Label: educ

                 Range: [1,6]                         Units: 1
         Unique values: 6                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                           43         1  No HS
                          280         2  High school graduate
                          216         3  Some college
                           99         4  2-year
                          225         5  4-year
                          137         6  Post-grad
                            1         .  

--------------------------------------------------------------------------------
female                                                                    Female
--------------------------------------------------------------------------------

                  Type: Numeric (float)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 0/1,001

            Tabulation: Freq.  Value
                          417  0
                          584  1

--------------------------------------------------------------------------------
faminc_clean                                                       Family income
--------------------------------------------------------------------------------

                  Type: Numeric (byte)
                 Label: faminc_new

                 Range: [1,16]                        Units: 1
         Unique values: 16                        Missing .: 96/1,001
       Unique mv codes: 2                        Missing .*: 1/1,001

              Examples: 3     $20,000 - $29,999
                        5     $40,000 - $49,999
                        8     $70,000 - $79,999
                        11    $120,000 - $149,999

. 
. **table 3. Unconditional experimental results
. sum eitc_support tanf_support

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
eitc_support |        997     3.34002    1.142345          1          5
tanf_support |        997    3.147442    1.181911          1          5

. ttest eitc_support, by(eitc_ineq)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
EITC fra |     501    3.341317    .0515095    1.152939    3.240116    3.442519
EITC ina |     496     3.33871      .05086    1.132706    3.238782    3.438638
---------+--------------------------------------------------------------------
Combined |     997     3.34002    .0361784    1.142345    3.269025    3.411015
---------+--------------------------------------------------------------------
    diff |            .0026077    .0723941               -.1394549    .1446703
------------------------------------------------------------------------------
    diff = mean(EITC fra) - mean(EITC ina)                        t =   0.0360
H0: diff = 0                                     Degrees of freedom =      995

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5144         Pr(|T| > |t|) = 0.9713          Pr(T > t) = 0.4856

. ttest tanf_support, by(tanf_ineq)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
TANF fra |     496    3.177419     .053866    1.199652    3.071585    3.283254
TANF ina |     501    3.117764    .0520269    1.164519    3.015546    3.219983
---------+--------------------------------------------------------------------
Combined |     997    3.147442    .0374315    1.181911    3.073989    3.220896
---------+--------------------------------------------------------------------
    diff |            .0596549    .0748777               -.0872814    .2065912
------------------------------------------------------------------------------
    diff = mean(TANF fra) - mean(TANF ina)                        t =   0.7967
H0: diff = 0                                     Degrees of freedom =      995

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7871         Pr(|T| > |t|) = 0.4258          Pr(T > t) = 0.2129

. 
. //test H1 & H2
. **eitc_ineq and ideology
. ologit eitc_support i.eitc_ineq##c.ideology

Iteration 0:  Log likelihood = -1484.4054  
Iteration 1:  Log likelihood = -1462.7514  
Iteration 2:  Log likelihood = -1462.6825  
Iteration 3:  Log likelihood = -1462.6825  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(3)    =  43.45
                                                        Prob > chi2   = 0.0000
Log likelihood = -1462.6825                             Pseudo R2     = 0.0146

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.6709239   .3198457    -2.10   0.036     -1.29781   -.0440379
     ideology |  -.4240066    .073263    -5.79   0.000    -.5675995   -.2804136
              |
    eitc_ineq#|
   c.ideology |
EITC inade..  |   .2051022   .1005183     2.04   0.041     .0080899    .4021145
--------------+----------------------------------------------------------------
        /cut1 |  -3.782424   .2678512                     -4.307403   -3.257446
        /cut2 |  -2.694046   .2505831                     -3.185179   -2.202912
        /cut3 |  -1.103847   .2383907                     -1.571084   -.6366101
        /cut4 |   .3142346   .2378368                     -.1519171    .7803862
-------------------------------------------------------------------------------

. eststo eitc_ideo 

. ologit eitc_support i.eitc_ineq##c.ideology $control 

Iteration 0:  Log likelihood = -1344.4926  
Iteration 1:  Log likelihood = -1316.3615  
Iteration 2:  Log likelihood = -1316.2262  
Iteration 3:  Log likelihood = -1316.2261  

Ordered logistic regression                             Number of obs =    902
                                                        LR chi2(9)    =  56.53
                                                        Prob > chi2   = 0.0000
Log likelihood = -1316.2261                             Pseudo R2     = 0.0210

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.7378509   .3359515    -2.20   0.028    -1.396304    -.079398
     ideology |  -.3004305   .0925728    -3.25   0.001    -.4818698   -.1189912
              |
    eitc_ineq#|
   c.ideology |
EITC inade..  |     .21208   .1056254     2.01   0.045     .0050581    .4191019
              |
   pid7_clean |  -.0922865   .0389184    -2.37   0.018    -.1685653   -.0160078
  age_ordinal |  -.0406157   .0352085    -1.15   0.249    -.1096231    .0283917
        White |  -.1428608   .1416146    -1.01   0.313    -.4204204    .1346988
         educ |  -.0447038   .0448513    -1.00   0.319    -.1326108    .0432032
       female |  -.0319095   .1238264    -0.26   0.797    -.2746048    .2107859
 faminc_clean |  -.0391268   .0196768    -1.99   0.047    -.0776927   -.0005609
--------------+----------------------------------------------------------------
        /cut1 |  -4.512592   .3783367                     -5.254119   -3.771066
        /cut2 |  -3.388779   .3609772                     -4.096281   -2.681277
        /cut3 |  -1.841977   .3491059                     -2.526212   -1.157742
        /cut4 |  -.3950768   .3470149                     -1.075213    .2850598
-------------------------------------------------------------------------------

. eststo eitc_ideo_cont

. 
. margins, predict(outcome(5)) over(eitc_ineq) at(ideology=(1(1)5))

Predictive margins                                         Number of obs = 902
Model VCE: OIM

Expression: Pr(eitc_support==5), predict(outcome(5))
Over:       eitc_ineq
1._at: 0.eitc_ineq
           ideology = 1
       1.eitc_ineq
           ideology = 1
2._at: 0.eitc_ineq
           ideology = 2
       1.eitc_ineq
           ideology = 2
3._at: 0.eitc_ineq
           ideology = 3
       1.eitc_ineq
           ideology = 3
4._at: 0.eitc_ineq
           ideology = 4
       1.eitc_ineq
           ideology = 4
5._at: 0.eitc_ineq
           ideology = 5
       1.eitc_ineq
           ideology = 5

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
_at#eitc_ineq |
           1 #|
EITC fraud..  |   .2884074   .0423175     6.82   0.000     .2054666    .3713481
           1 #|
EITC inade..  |   .2011698   .0303129     6.64   0.000     .1417577     .260582
           2 #|
EITC fraud..  |   .2317115   .0240681     9.63   0.000      .184539     .278884
           2 #|
EITC inade..  |   .1875329   .0191727     9.78   0.000     .1499551    .2251108
           3 #|
EITC fraud..  |   .1831842   .0156123    11.73   0.000     .1525846    .2137838
           3 #|
EITC inade..  |   .1746093   .0154467    11.30   0.000     .1443344    .2048841
           4 #|
EITC fraud..  |   .1428428   .0175267     8.15   0.000      .108491    .1771946
           4 #|
EITC inade..  |   .1623897    .020448     7.94   0.000     .1223123     .202467
           5 #|
EITC fraud..  |   .1101227   .0212968     5.17   0.000     .0683817    .1518637
           5 #|
EITC inade..  |    .150861    .028423     5.31   0.000     .0951529    .2065691
-------------------------------------------------------------------------------

. marginsplot, title("EITC experiment") ytitle("Predicted probability of strong 
> support") xtitle("") legend(ring(0) pos(7) col(1)) xlabel(, angle(45) labsize(
> small))ylabel(0(.1).4) recast(line) recastci(rarea) ci1opts(fcolor(%30)) ci2op
> ts(fcolor(%30)) saving(Figures/eitc_ideo, replace)

Variables that uniquely identify margins: ideology eitc_ineq
file Figures/eitc_ideo.gph saved

. 
. **tanf_ineq and ideology
. ologit tanf_support i.tanf_ineq##c.ideology 

Iteration 0:  Log likelihood = -1525.7774  
Iteration 1:  Log likelihood = -1496.1854  
Iteration 2:  Log likelihood = -1496.0574  
Iteration 3:  Log likelihood = -1496.0574  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(3)    =  59.44
                                                        Prob > chi2   = 0.0000
Log likelihood = -1496.0574                             Pseudo R2     = 0.0195

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |  -.2272375    .324714    -0.70   0.484    -.8636652    .4091902
     ideology |  -.4258598    .073043    -5.83   0.000    -.5690214   -.2826982
              |
    tanf_ineq#|
   c.ideology |
TANF inade..  |    .065725   .1016974     0.65   0.518    -.1335984    .2650483
--------------+----------------------------------------------------------------
        /cut1 |  -3.530355   .2531733                     -4.026565   -3.034144
        /cut2 |  -2.268226   .2365844                     -2.731922   -1.804529
        /cut3 |  -.7811842   .2266876                     -1.225484   -.3368846
        /cut4 |   .5225629   .2285761                       .074562    .9705638
-------------------------------------------------------------------------------

. eststo tanf_ideo

. testparm i.tanf_ineq#c.ideology##c.ideology

 ( 1)  [tanf_support]ideology = 0
 ( 2)  [tanf_support]1.tanf_ineq#c.ideology = 0

           chi2(  2) =   57.26
         Prob > chi2 =    0.0000

. di r(p)
3.676e-13

. ologit tanf_support i.tanf_ineq##c.ideology  $control 

Iteration 0:  Log likelihood = -1386.0474  
Iteration 1:  Log likelihood = -1335.0322  
Iteration 2:  Log likelihood = -1334.6038  
Iteration 3:  Log likelihood = -1334.6034  
Iteration 4:  Log likelihood = -1334.6034  

Ordered logistic regression                             Number of obs =    901
                                                        LR chi2(9)    = 102.89
                                                        Prob > chi2   = 0.0000
Log likelihood = -1334.6034                             Pseudo R2     = 0.0371

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |  -.0972432   .3424747    -0.28   0.776    -.7684814    .5739949
     ideology |  -.1620815   .0915149    -1.77   0.077    -.3414475    .0172844
              |
    tanf_ineq#|
   c.ideology |
TANF inade..  |   .0163006    .107363     0.15   0.879    -.1941269    .2267282
              |
   pid7_clean |  -.1536006   .0398465    -3.85   0.000    -.2316983    -.075503
  age_ordinal |  -.1506656   .0356346    -4.23   0.000    -.2205082   -.0808231
        White |  -.1510733    .141617    -1.07   0.286    -.4286376     .126491
         educ |   .0068944   .0445306     0.15   0.877     -.080384    .0941727
       female |  -.0812807   .1235807    -0.66   0.511    -.3234944     .160933
 faminc_clean |  -.0588552   .0197624    -2.98   0.003    -.0975888   -.0201215
--------------+----------------------------------------------------------------
        /cut1 |  -4.441579   .3538708                     -5.135154   -3.748005
        /cut2 |  -3.079977   .3361469                     -3.738812   -2.421141
        /cut3 |  -1.605382   .3255213                     -2.243392   -.9673722
        /cut4 |  -.2977423   .3246129                     -.9339719    .3384872
-------------------------------------------------------------------------------

. eststo tanf_ideo_cont

. 
. margins, predict(outcome(5)) over(tanf_ineq) at(ideology=(1(1)5))

Predictive margins                                         Number of obs = 901
Model VCE: OIM

Expression: Pr(tanf_support==5), predict(outcome(5))
Over:       tanf_ineq
1._at: 0.tanf_ineq
           ideology = 1
       1.tanf_ineq
           ideology = 1
2._at: 0.tanf_ineq
           ideology = 2
       1.tanf_ineq
           ideology = 2
3._at: 0.tanf_ineq
           ideology = 3
       1.tanf_ineq
           ideology = 3
4._at: 0.tanf_ineq
           ideology = 4
       1.tanf_ineq
           ideology = 4
5._at: 0.tanf_ineq
           ideology = 5
       1.tanf_ineq
           ideology = 5

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
_at#tanf_ineq |
           1 #|
TANF fraud..  |   .2108713   .0307733     6.85   0.000     .1505566    .2711859
           1 #|
TANF inade..  |     .18735   .0311562     6.01   0.000      .126285    .2484149
           2 #|
TANF fraud..  |   .1861405   .0186835     9.96   0.000     .1495216    .2227595
           2 #|
TANF inade..  |   .1668297   .0189874     8.79   0.000     .1296152    .2040443
           3 #|
TANF fraud..  |   .1636295   .0148822    10.99   0.000     .1344609     .192798
           3 #|
TANF inade..  |   .1480936   .0138218    10.71   0.000     .1210034    .1751837
           4 #|
TANF fraud..  |   .1432918   .0191557     7.48   0.000     .1057474    .1808363
           4 #|
TANF inade..  |   .1310837   .0169603     7.73   0.000     .0978421    .1643253
           5 #|
TANF fraud..  |   .1250444    .025204     4.96   0.000     .0756454    .1744434
           5 #|
TANF inade..  |   .1157221   .0227481     5.09   0.000     .0711367    .1603076
-------------------------------------------------------------------------------

. marginsplot, title("TANF experiment") ytitle("Predicted probability of strong 
> support") xtitle("") legend(ring(0) pos(7) col(1)) xscale(r(0.75 5.25)) xlabel
> (, angle(45) labsize(small))ylabel(0(.1).4) recast(line) recastci(rarea) ci1op
> ts(fcolor(%30)) ci2opts(fcolor(%30)) saving(Figures/tanf_ideo, replace)

Variables that uniquely identify margins: ideology tanf_ineq
file Figures/tanf_ideo.gph saved

. 
. **Figure 1. Combined ideology interaction graph
. gr combine Figures/eitc_ideo.gph Figures/tanf_ideo.gph

. gr export Figures/ideo_linear.png, as(png) replace
file Figures/ideo_linear.png saved as PNG format

. 
. ******************************************************************************
> ******
. *** Part 2. Appendix
. ******************************************************************************
> ******
. ****************Appendix B. Descriptive Statistics of Survey Sample***********
> ******
. **table B.1.
. codebook pid7_clean age_ordinal White educ female faminc_clean ideology

--------------------------------------------------------------------------------
pid7_clean                                                      7 point Party ID
--------------------------------------------------------------------------------

                  Type: Numeric (byte)
                 Label: pid7

                 Range: [1,7]                         Units: 1
         Unique values: 7                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          259         1  Strong Democrat
                          120         2  Not very strong Democrat
                          107         3  Lean Democrat
                          170         4  Independent
                           88         5  Lean Republican
                           82         6  Not very strong Republican
                          174         7  Strong Republican
                            1         .  

--------------------------------------------------------------------------------
age_ordinal                                               Age (6-category scale)
--------------------------------------------------------------------------------

                  Type: Numeric (float)
                 Label: age

                 Range: [1,6]                         Units: 1
         Unique values: 6                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          197         1  18-29
                          158         2  30-39
                          159         3  40-49
                          172         4  50-59
                          120         5  60-65
                          194         6  65+
                            1         .  

--------------------------------------------------------------------------------
White                                                                      White
--------------------------------------------------------------------------------

                  Type: Numeric (float)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 1/1,001

            Tabulation: Freq.  Value
                          279  0
                          721  1
                            1  .

--------------------------------------------------------------------------------
educ                                                                   Education
--------------------------------------------------------------------------------

                  Type: Numeric (byte)
                 Label: educ

                 Range: [1,6]                         Units: 1
         Unique values: 6                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                           43         1  No HS
                          280         2  High school graduate
                          216         3  Some college
                           99         4  2-year
                          225         5  4-year
                          137         6  Post-grad
                            1         .  

--------------------------------------------------------------------------------
female                                                                    Female
--------------------------------------------------------------------------------

                  Type: Numeric (float)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 0/1,001

            Tabulation: Freq.  Value
                          417  0
                          584  1

--------------------------------------------------------------------------------
faminc_clean                                                       Family income
--------------------------------------------------------------------------------

                  Type: Numeric (byte)
                 Label: faminc_new

                 Range: [1,16]                        Units: 1
         Unique values: 16                        Missing .: 96/1,001
       Unique mv codes: 2                        Missing .*: 1/1,001

              Examples: 3     $20,000 - $29,999
                        5     $40,000 - $49,999
                        8     $70,000 - $79,999
                        11    $120,000 - $149,999

--------------------------------------------------------------------------------
ideology                                                                Ideology
--------------------------------------------------------------------------------

                  Type: Numeric (byte)
                 Label: ideo5

                 Range: [1,5]                         Units: 1
         Unique values: 5                         Missing .: 1/1,001

            Tabulation: Freq.   Numeric  Label
                          120         1  Very liberal
                          180         2  Liberal
                          412         3  Moderate
                          175         4  Conservative
                          113         5  Very conservative
                            1         .  

. estpost summarize pid7_clean age_ordinal White educ female faminc_clean ideolo
> gy

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min) 
-------------+------------------------------------------------------------------
  pid7_clean |      1000       1000       3.65   4.782282   2.186843          1 
 age_ordinal |      1000       1000      3.442   3.151788   1.775328          1 
       White |      1000       1000       .721   .2013604   .4487319          0 
        educ |      1000       1000      3.594   2.333497   1.527579          1 
      female |      1001       1001   .5834166   .2432847    .493239          0 
faminc_clean |       904        904   6.130531   11.88992   3.448176          1 
    ideology |      1000       1000      2.981   1.287927   1.134869          1 

             |    e(max)     e(sum) 
-------------+----------------------
  pid7_clean |         7       3650 
 age_ordinal |         6       3442 
       White |         1        721 
        educ |         6       3594 
      female |         1        584 
faminc_clean |        16       5542 
    ideology |         5       2981 

. 
. ********************Appendix D. Ordered Logistic Regression Results***********
> ******
. global control "pid7_clean age_ordinal White educ female faminc_clean"

. 
. **table D.1. (eitc_ineq and ideology)
. ologit eitc_support i.eitc_ineq##c.ideology 

Iteration 0:  Log likelihood = -1484.4054  
Iteration 1:  Log likelihood = -1462.7514  
Iteration 2:  Log likelihood = -1462.6825  
Iteration 3:  Log likelihood = -1462.6825  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(3)    =  43.45
                                                        Prob > chi2   = 0.0000
Log likelihood = -1462.6825                             Pseudo R2     = 0.0146

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.6709239   .3198457    -2.10   0.036     -1.29781   -.0440379
     ideology |  -.4240066    .073263    -5.79   0.000    -.5675995   -.2804136
              |
    eitc_ineq#|
   c.ideology |
EITC inade..  |   .2051022   .1005183     2.04   0.041     .0080899    .4021145
--------------+----------------------------------------------------------------
        /cut1 |  -3.782424   .2678512                     -4.307403   -3.257446
        /cut2 |  -2.694046   .2505831                     -3.185179   -2.202912
        /cut3 |  -1.103847   .2383907                     -1.571084   -.6366101
        /cut4 |   .3142346   .2378368                     -.1519171    .7803862
-------------------------------------------------------------------------------

. eststo eitc_ideo

. ologit eitc_support i.eitc_ineq##c.ideology $control

Iteration 0:  Log likelihood = -1344.4926  
Iteration 1:  Log likelihood = -1316.3615  
Iteration 2:  Log likelihood = -1316.2262  
Iteration 3:  Log likelihood = -1316.2261  

Ordered logistic regression                             Number of obs =    902
                                                        LR chi2(9)    =  56.53
                                                        Prob > chi2   = 0.0000
Log likelihood = -1316.2261                             Pseudo R2     = 0.0210

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.7378509   .3359515    -2.20   0.028    -1.396304    -.079398
     ideology |  -.3004305   .0925728    -3.25   0.001    -.4818698   -.1189912
              |
    eitc_ineq#|
   c.ideology |
EITC inade..  |     .21208   .1056254     2.01   0.045     .0050581    .4191019
              |
   pid7_clean |  -.0922865   .0389184    -2.37   0.018    -.1685653   -.0160078
  age_ordinal |  -.0406157   .0352085    -1.15   0.249    -.1096231    .0283917
        White |  -.1428608   .1416146    -1.01   0.313    -.4204204    .1346988
         educ |  -.0447038   .0448513    -1.00   0.319    -.1326108    .0432032
       female |  -.0319095   .1238264    -0.26   0.797    -.2746048    .2107859
 faminc_clean |  -.0391268   .0196768    -1.99   0.047    -.0776927   -.0005609
--------------+----------------------------------------------------------------
        /cut1 |  -4.512592   .3783367                     -5.254119   -3.771066
        /cut2 |  -3.388779   .3609772                     -4.096281   -2.681277
        /cut3 |  -1.841977   .3491059                     -2.526212   -1.157742
        /cut4 |  -.3950768   .3470149                     -1.075213    .2850598
-------------------------------------------------------------------------------

. eststo eitc_ideo_cont

. 
. esttab eitc_ideo eitc_ideo_cont using Tables/eitc_ideo.txt, tab se(2) b(2) pr2
>  onecell label  nobaselevels nomtitles eqlabels(none) interaction(" X ") scala
> rs(N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" cut3 "Cutpoint 3" cut4 "C
> utpoint 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/eitc_ideo.txt)

. 
. **table D.2. (tanf_ineq and ideology)
. ologit tanf_support i.tanf_ineq##c.ideology 

Iteration 0:  Log likelihood = -1525.7774  
Iteration 1:  Log likelihood = -1496.1854  
Iteration 2:  Log likelihood = -1496.0574  
Iteration 3:  Log likelihood = -1496.0574  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(3)    =  59.44
                                                        Prob > chi2   = 0.0000
Log likelihood = -1496.0574                             Pseudo R2     = 0.0195

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |  -.2272375    .324714    -0.70   0.484    -.8636652    .4091902
     ideology |  -.4258598    .073043    -5.83   0.000    -.5690214   -.2826982
              |
    tanf_ineq#|
   c.ideology |
TANF inade..  |    .065725   .1016974     0.65   0.518    -.1335984    .2650483
--------------+----------------------------------------------------------------
        /cut1 |  -3.530355   .2531733                     -4.026565   -3.034144
        /cut2 |  -2.268226   .2365844                     -2.731922   -1.804529
        /cut3 |  -.7811842   .2266876                     -1.225484   -.3368846
        /cut4 |   .5225629   .2285761                       .074562    .9705638
-------------------------------------------------------------------------------

. eststo tanf_ideo

. ologit tanf_support i.tanf_ineq##c.ideology  $control

Iteration 0:  Log likelihood = -1386.0474  
Iteration 1:  Log likelihood = -1335.0322  
Iteration 2:  Log likelihood = -1334.6038  
Iteration 3:  Log likelihood = -1334.6034  
Iteration 4:  Log likelihood = -1334.6034  

Ordered logistic regression                             Number of obs =    901
                                                        LR chi2(9)    = 102.89
                                                        Prob > chi2   = 0.0000
Log likelihood = -1334.6034                             Pseudo R2     = 0.0371

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |  -.0972432   .3424747    -0.28   0.776    -.7684814    .5739949
     ideology |  -.1620815   .0915149    -1.77   0.077    -.3414475    .0172844
              |
    tanf_ineq#|
   c.ideology |
TANF inade..  |   .0163006    .107363     0.15   0.879    -.1941269    .2267282
              |
   pid7_clean |  -.1536006   .0398465    -3.85   0.000    -.2316983    -.075503
  age_ordinal |  -.1506656   .0356346    -4.23   0.000    -.2205082   -.0808231
        White |  -.1510733    .141617    -1.07   0.286    -.4286376     .126491
         educ |   .0068944   .0445306     0.15   0.877     -.080384    .0941727
       female |  -.0812807   .1235807    -0.66   0.511    -.3234944     .160933
 faminc_clean |  -.0588552   .0197624    -2.98   0.003    -.0975888   -.0201215
--------------+----------------------------------------------------------------
        /cut1 |  -4.441579   .3538708                     -5.135154   -3.748005
        /cut2 |  -3.079977   .3361469                     -3.738812   -2.421141
        /cut3 |  -1.605382   .3255213                     -2.243392   -.9673722
        /cut4 |  -.2977423   .3246129                     -.9339719    .3384872
-------------------------------------------------------------------------------

. eststo tanf_ideo_cont

. 
. esttab tanf_ideo tanf_ideo_cont using Tables/tanf_ideo.txt, tab se(2) b(2) pr2
>  onecell label  nobaselevels nomtitles eqlabels(none) interaction(" X ")  scal
> ars(N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" cut3 "Cutpoint 3" cut4 "
> Cutpoint 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/tanf_ideo.txt)

. 
. **table D.3. (eitc_ineq, ideology, and political knowledge)
. ologit eitc_support i.eitc_ineq##c.ideology##c.polknow 

Iteration 0:  Log likelihood = -1484.4054  
Iteration 1:  Log likelihood = -1457.4876  
Iteration 2:  Log likelihood = -1457.3722  
Iteration 3:  Log likelihood = -1457.3722  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(7)    =  54.07
                                                        Prob > chi2   = 0.0000
Log likelihood = -1457.3722                             Pseudo R2     = 0.0182

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.6733301   .8874435    -0.76   0.448    -2.412687    1.066027
     ideology |   .0026792    .209797     0.01   0.990    -.4085154    .4138737
              |
    eitc_ineq#|
   c.ideology |
EITC inade..  |   .1473783   .2823316     0.52   0.602    -.4059815     .700738
              |
      polknow |   .1847195   .1026083     1.80   0.072     -.016389     .385828
              |
    eitc_ineq#|
    c.polknow |
EITC inade..  |   .0123167   .1364947     0.09   0.928     -.255208    .2798415
              |
   c.ideology#|
    c.polknow |  -.0699147   .0319002    -2.19   0.028    -.1324381   -.0073914
              |
    eitc_ineq#|
   c.ideology#|
    c.polknow |
EITC inade..  |   .0072279   .0436719     0.17   0.869    -.0783674    .0928233
--------------+----------------------------------------------------------------
        /cut1 |  -2.638731   .6836123                     -3.978586   -1.298875
        /cut2 |  -1.543193   .6782449                     -2.872529   -.2138574
        /cut3 |   .0553538   .6763576                     -1.270283     1.38099
        /cut4 |   1.482501   .6787506                      .1521743    2.812828
-------------------------------------------------------------------------------

. eststo eitc_ideo_polknow

. ologit eitc_support i.eitc_ineq##c.ideology##c.polknow $control

Iteration 0:  Log likelihood = -1344.4926  
Iteration 1:  Log likelihood = -1311.5756  
Iteration 2:  Log likelihood =  -1311.381  
Iteration 3:  Log likelihood = -1311.3809  

Ordered logistic regression                             Number of obs =    902
                                                        LR chi2(13)   =  66.22
                                                        Prob > chi2   = 0.0000
Log likelihood = -1311.3809                             Pseudo R2     = 0.0246

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |   -.600351   .9623435    -0.62   0.533     -2.48651    1.285808
     ideology |   .1190329   .2353119     0.51   0.613    -.3421699    .5802358
              |
    eitc_ineq#|
   c.ideology |
EITC inade..  |   .1210098   .3040508     0.40   0.691    -.4749187    .7169384
              |
      polknow |   .2282464   .1154957     1.98   0.048      .001879    .4546137
              |
    eitc_ineq#|
    c.polknow |
EITC inade..  |  -.0083302   .1472792    -0.06   0.955    -.2969921    .2803318
              |
   c.ideology#|
    c.polknow |  -.0687359   .0350108    -1.96   0.050    -.1373559   -.0001159
              |
    eitc_ineq#|
   c.ideology#|
    c.polknow |
EITC inade..  |   .0118515   .0468748     0.25   0.800    -.0800214    .1037245
              |
   pid7_clean |  -.0831445   .0391205    -2.13   0.034    -.1598193   -.0064696
  age_ordinal |  -.0560937   .0380295    -1.48   0.140    -.1306302    .0184428
        White |  -.1747822   .1427384    -1.22   0.221    -.4545443      .10498
         educ |  -.0660563   .0471146    -1.40   0.161    -.1583992    .0262866
       female |   .0080554    .126395     0.06   0.949    -.2396742    .2557851
 faminc_clean |  -.0463609   .0202929    -2.28   0.022    -.0861343   -.0065875
--------------+----------------------------------------------------------------
        /cut1 |  -3.277171   .7885554                     -4.822712   -1.731631
        /cut2 |  -2.147383    .782477                      -3.68101   -.6137567
        /cut3 |  -.5914999    .779515                     -2.119321    .9363215
        /cut4 |   .8666308   .7805503                     -.6632196    2.396481
-------------------------------------------------------------------------------

. eststo etic_ideo_polknow_cont

. 
. esttab eitc_ideo_polknow etic_ideo_polknow_cont using Tables/eitc_ideo_polknow
> .txt, tab se(2) b(2) pr2 onecell label  nobaselevels nomtitles eqlabels(none) 
> interaction(" X ")  scalars(N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" 
> cut3 "Cutpoint 3" cut4 "Cutpoint 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/eitc_ideo_polknow.txt)

. 
. **table D.4. (tanf_ineq, ideology, and political knowledge)
. ologit tanf_support i.tanf_ineq##c.ideology##c.polknow 

Iteration 0:  Log likelihood = -1525.7774  
Iteration 1:  Log likelihood = -1488.4452  
Iteration 2:  Log likelihood = -1488.2207  
Iteration 3:  Log likelihood = -1488.2206  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(7)    =  75.11
                                                        Prob > chi2   = 0.0000
Log likelihood = -1488.2206                             Pseudo R2     = 0.0246

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |   .2739876   .9096096     0.30   0.763    -1.508814     2.05679
     ideology |  -.0409353   .1972148    -0.21   0.836    -.4274692    .3455987
              |
    tanf_ineq#|
   c.ideology |
TANF inade..  |  -.1028413   .2893732    -0.36   0.722    -.6700024    .4643198
              |
      polknow |   .1303498   .0938545     1.39   0.165    -.0536017    .3143012
              |
    tanf_ineq#|
    c.polknow |
TANF inade..  |  -.0883903   .1395467    -0.63   0.526    -.3618968    .1851163
              |
   c.ideology#|
    c.polknow |  -.0662779   .0309967    -2.14   0.032    -.1270303   -.0055254
              |
    tanf_ineq#|
   c.ideology#|
    c.polknow |
TANF inade..  |   .0301073   .0445172     0.68   0.499    -.0571449    .1173594
--------------+----------------------------------------------------------------
        /cut1 |   -2.76417   .6082747                     -3.956366   -1.571973
        /cut2 |  -1.483259   .6026816                     -2.664493   -.3020247
        /cut3 |    .020996   .6008023                     -1.156555    1.198547
        /cut4 |   1.330039   .6037728                      .1466659    2.513412
-------------------------------------------------------------------------------

. eststo tanf_ideo_polknow

. ologit tanf_support i.tanf_ineq##c.ideology##c.polknow $control

Iteration 0:  Log likelihood = -1386.0474  
Iteration 1:  Log likelihood = -1333.2777  
Iteration 2:  Log likelihood = -1332.7831  
Iteration 3:  Log likelihood = -1332.7826  
Iteration 4:  Log likelihood = -1332.7826  

Ordered logistic regression                             Number of obs =    901
                                                        LR chi2(13)   = 106.53
                                                        Prob > chi2   = 0.0000
Log likelihood = -1332.7826                             Pseudo R2     = 0.0384

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |   .4488972   .9895793     0.45   0.650    -1.490643    2.388437
     ideology |   .1102732   .2062796     0.53   0.593    -.2940275    .5145738
              |
    tanf_ineq#|
   c.ideology |
TANF inade..  |  -.1319748   .3126938    -0.42   0.673    -.7448434    .4808938
              |
      polknow |   .1290288   .0993981     1.30   0.194    -.0657879    .3238456
              |
    tanf_ineq#|
    c.polknow |
TANF inade..  |  -.0955169   .1509445    -0.63   0.527    -.3913626    .2003289
              |
   c.ideology#|
    c.polknow |  -.0482224   .0324267    -1.49   0.137    -.1117776    .0153327
              |
    tanf_ineq#|
   c.ideology#|
    c.polknow |
TANF inade..  |   .0258386   .0479633     0.54   0.590    -.0681678    .1198451
              |
   pid7_clean |  -.1471411   .0399897    -3.68   0.000    -.2255194   -.0687627
  age_ordinal |  -.1358565   .0386255    -3.52   0.000     -.211561   -.0601519
        White |  -.1532274   .1424668    -1.08   0.282    -.4324573    .1260024
         educ |   .0150603    .046593     0.32   0.747    -.0762603     .106381
       female |  -.0968892   .1258295    -0.77   0.441    -.3435106    .1497321
 faminc_clean |  -.0551072   .0203203    -2.71   0.007    -.0949342   -.0152801
--------------+----------------------------------------------------------------
        /cut1 |  -3.595716   .6595675                     -4.888445   -2.302987
        /cut2 |  -2.227749   .6518363                     -3.505324   -.9501732
        /cut3 |  -.7479311   .6486146                     -2.019192    .5233302
        /cut4 |   .5623014    .650386                     -.7124318    1.837035
-------------------------------------------------------------------------------

. eststo tanf_ideo_polknow_cont

. 
. esttab tanf_ideo_polknow tanf_ideo_polknow_cont using Tables/tanf_ideo_polknow
> .txt, tab se(2) b(2) pr2 onecell label  nobaselevels nomtitles eqlabels(none) 
> interaction(" X ")  scalars(N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" 
> cut3 "Cutpoint 3" cut4 "Cutpoint 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/tanf_ideo_polknow.txt)

. 
. **figure D.1.
. ologit eitc_support i.eitc_ineq##c.ideology##c.polknow $control

Iteration 0:  Log likelihood = -1344.4926  
Iteration 1:  Log likelihood = -1311.5756  
Iteration 2:  Log likelihood =  -1311.381  
Iteration 3:  Log likelihood = -1311.3809  

Ordered logistic regression                             Number of obs =    902
                                                        LR chi2(13)   =  66.22
                                                        Prob > chi2   = 0.0000
Log likelihood = -1311.3809                             Pseudo R2     = 0.0246

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |   -.600351   .9623435    -0.62   0.533     -2.48651    1.285808
     ideology |   .1190329   .2353119     0.51   0.613    -.3421699    .5802358
              |
    eitc_ineq#|
   c.ideology |
EITC inade..  |   .1210098   .3040508     0.40   0.691    -.4749187    .7169384
              |
      polknow |   .2282464   .1154957     1.98   0.048      .001879    .4546137
              |
    eitc_ineq#|
    c.polknow |
EITC inade..  |  -.0083302   .1472792    -0.06   0.955    -.2969921    .2803318
              |
   c.ideology#|
    c.polknow |  -.0687359   .0350108    -1.96   0.050    -.1373559   -.0001159
              |
    eitc_ineq#|
   c.ideology#|
    c.polknow |
EITC inade..  |   .0118515   .0468748     0.25   0.800    -.0800214    .1037245
              |
   pid7_clean |  -.0831445   .0391205    -2.13   0.034    -.1598193   -.0064696
  age_ordinal |  -.0560937   .0380295    -1.48   0.140    -.1306302    .0184428
        White |  -.1747822   .1427384    -1.22   0.221    -.4545443      .10498
         educ |  -.0660563   .0471146    -1.40   0.161    -.1583992    .0262866
       female |   .0080554    .126395     0.06   0.949    -.2396742    .2557851
 faminc_clean |  -.0463609   .0202929    -2.28   0.022    -.0861343   -.0065875
--------------+----------------------------------------------------------------
        /cut1 |  -3.277171   .7885554                     -4.822712   -1.731631
        /cut2 |  -2.147383    .782477                      -3.68101   -.6137567
        /cut3 |  -.5914999    .779515                     -2.119321    .9363215
        /cut4 |   .8666308   .7805503                     -.6632196    2.396481
-------------------------------------------------------------------------------

. margins, predict(outcome(5)) dydx(eitc_ineq) at(ideology=(1(1)5) polknow=(0 8)
> )

Average marginal effects                                   Number of obs = 902
Model VCE: OIM

Expression: Pr(eitc_support==5), predict(outcome(5))
dy/dx wrt:  1.eitc_ineq
1._at:  ideology = 1
        polknow  = 0
2._at:  ideology = 1
        polknow  = 8
3._at:  ideology = 2
        polknow  = 0
4._at:  ideology = 2
        polknow  = 8
5._at:  ideology = 3
        polknow  = 0
6._at:  ideology = 3
        polknow  = 8
7._at:  ideology = 4
        polknow  = 0
8._at:  ideology = 4
        polknow  = 8
9._at:  ideology = 5
        polknow  = 0
10._at: ideology = 5
        polknow  = 8

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.eitc_ineq  |  (base outcome)
-------------+----------------------------------------------------------------
1.eitc_ineq  |
         _at |
          1  |  -.0469371   .0706856    -0.66   0.507    -.1854782     .091604
          2  |  -.0936175   .0652213    -1.44   0.151    -.2214488    .0342138
          3  |  -.0400074   .0483594    -0.83   0.408    -.1347901    .0547752
          4  |   -.042577     .03801    -1.12   0.263    -.1170753    .0319213
          5  |  -.0300166   .0342883    -0.88   0.381    -.0972205    .0371873
          6  |  -.0029623   .0256125    -0.12   0.908    -.0531619    .0472372
          7  |  -.0165382   .0563223    -0.29   0.769    -.1269278    .0938514
          8  |   .0241928   .0285186     0.85   0.396    -.0317025    .0800882
          9  |   .0007443     .10347     0.01   0.994    -.2020533    .2035419
         10  |   .0402682    .034505     1.17   0.243    -.0273605    .1078968
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, title("EITC") ytitle("Inadequacy treatment effect on" "probabilit
> y of strong support") xtitle("") xscale(r(.75 5.25)) xlabel(, angle(45)) recas
> t(line) recastci(rarea) ci1opts(fcolor(%30)) ci2opts(fcolor(%30)) legend(order
> (3 "Low knowledge" 4 "High knowledge")) yline(0, lpattern(dot) lcolor(gs8)) sa
> ving(Figures/eitc_ideopk, replace)

Variables that uniquely identify margins: ideology polknow
file Figures/eitc_ideopk.gph saved

. 
. ologit tanf_support i.tanf_ineq##c.ideology##c.polknow $control

Iteration 0:  Log likelihood = -1386.0474  
Iteration 1:  Log likelihood = -1333.2777  
Iteration 2:  Log likelihood = -1332.7831  
Iteration 3:  Log likelihood = -1332.7826  
Iteration 4:  Log likelihood = -1332.7826  

Ordered logistic regression                             Number of obs =    901
                                                        LR chi2(13)   = 106.53
                                                        Prob > chi2   = 0.0000
Log likelihood = -1332.7826                             Pseudo R2     = 0.0384

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |   .4488972   .9895793     0.45   0.650    -1.490643    2.388437
     ideology |   .1102732   .2062796     0.53   0.593    -.2940275    .5145738
              |
    tanf_ineq#|
   c.ideology |
TANF inade..  |  -.1319748   .3126938    -0.42   0.673    -.7448434    .4808938
              |
      polknow |   .1290288   .0993981     1.30   0.194    -.0657879    .3238456
              |
    tanf_ineq#|
    c.polknow |
TANF inade..  |  -.0955169   .1509445    -0.63   0.527    -.3913626    .2003289
              |
   c.ideology#|
    c.polknow |  -.0482224   .0324267    -1.49   0.137    -.1117776    .0153327
              |
    tanf_ineq#|
   c.ideology#|
    c.polknow |
TANF inade..  |   .0258386   .0479633     0.54   0.590    -.0681678    .1198451
              |
   pid7_clean |  -.1471411   .0399897    -3.68   0.000    -.2255194   -.0687627
  age_ordinal |  -.1358565   .0386255    -3.52   0.000     -.211561   -.0601519
        White |  -.1532274   .1424668    -1.08   0.282    -.4324573    .1260024
         educ |   .0150603    .046593     0.32   0.747    -.0762603     .106381
       female |  -.0968892   .1258295    -0.77   0.441    -.3435106    .1497321
 faminc_clean |  -.0551072   .0203203    -2.71   0.007    -.0949342   -.0152801
--------------+----------------------------------------------------------------
        /cut1 |  -3.595716   .6595675                     -4.888445   -2.302987
        /cut2 |  -2.227749   .6518363                     -3.505324   -.9501732
        /cut3 |  -.7479311   .6486146                     -2.019192    .5233302
        /cut4 |   .5623014    .650386                     -.7124318    1.837035
-------------------------------------------------------------------------------

. margins, predict(outcome(5)) dydx(tanf_ineq) at(ideology=(1(1)5) polknow=(0 8)
> )

Average marginal effects                                   Number of obs = 901
Model VCE: OIM

Expression: Pr(tanf_support==5), predict(outcome(5))
dy/dx wrt:  1.tanf_ineq
1._at:  ideology = 1
        polknow  = 0
2._at:  ideology = 1
        polknow  = 8
3._at:  ideology = 2
        polknow  = 0
4._at:  ideology = 2
        polknow  = 8
5._at:  ideology = 3
        polknow  = 0
6._at:  ideology = 3
        polknow  = 8
7._at:  ideology = 4
        polknow  = 0
8._at:  ideology = 4
        polknow  = 8
9._at:  ideology = 5
        polknow  = 0
10._at: ideology = 5
        polknow  = 8

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.tanf_ineq  |  (base outcome)
-------------+----------------------------------------------------------------
1.tanf_ineq  |
         _at |
          1  |   .0411869    .092966     0.44   0.658    -.1410232     .223397
          2  |  -.0388096   .0511461    -0.76   0.448     -.139054    .0614349
          3  |   .0247132   .0576228     0.43   0.668    -.0882254    .1376517
          4  |  -.0233504   .0296159    -0.79   0.430    -.0813965    .0346957
          5  |   .0072759   .0372854     0.20   0.845    -.0658023     .080354
          6  |  -.0110097   .0202011    -0.55   0.586    -.0506031    .0285836
          7  |  -.0111495   .0569566    -0.20   0.845    -.1227824    .1004833
          8  |  -.0016683   .0231023    -0.07   0.942    -.0469479    .0436114
          9  |  -.0305792   .0969114    -0.32   0.752    -.2205221    .1593637
         10  |   .0049988   .0287564     0.17   0.862    -.0513627    .0613604
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, title("TANF") ytitle("Inadequacy treatment effect on" "probabilit
> y of strong support") xtitle("") xscale(r(.75 5.25)) xlabel(, angle(45)) recas
> t(line) recastci(rarea) ci1opts(fcolor(%30)) ci2opts(fcolor(%30)) legend(order
> (3 "Low knowledge" 4 "High knowledge")) yline(0, lpattern(dot) lcolor(gs8)) sa
> ving(Figures/tanf_ideopk, replace)

Variables that uniquely identify margins: ideology polknow
file Figures/tanf_ideopk.gph saved

. 
. *combined graph - must install grc1leg (findit grc1leg)
. grc1leg Figures/eitc_ideopk.gph Figures/tanf_ideopk.gph 

. gr export Figures/ideopk.png, as(png) replace
file Figures/ideopk.png saved as PNG format

. 
. **table D.5. (tanf, combined inadequacy treatment, and ideology)
. use CCES_expanded.dta, clear
(CCES 2020 expanded module data for Qi & Haselswerdt policy info experiment, 202
> 1)

. global control "pid7_clean age_ordinal White educ female faminc_clean"

. ologit support i.experiment##i.combined_ineq##c.ideology, vce(cluster caseid) 

Iteration 0:  Log pseudolikelihood =  -3019.633  
Iteration 1:  Log pseudolikelihood = -2960.8385  
Iteration 2:  Log pseudolikelihood = -2960.5838  
Iteration 3:  Log pseudolikelihood = -2960.5837  

Ordered logistic regression                             Number of obs =  1,994
                                                        Wald chi2(7)  = 103.63
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -2960.5837                       Pseudo R2     = 0.0196

                              (Std. err. adjusted for 1,000 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
      support | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
   experiment |
        TANF  |  -.2758832   .3357734    -0.82   0.411     -.933987    .3822206
1.combined_~q |  -.6575808   .3050809    -2.16   0.031    -1.255528   -.0596333
              |
   experiment#|
combined_ineq |
      TANF#1  |   .4280519     .53877     0.79   0.427    -.6279178    1.484022
              |
     ideology |  -.4159936   .0716144    -5.81   0.000    -.5563552    -.275632
              |
   experiment#|
   c.ideology |
        TANF  |  -.0177161   .1071464    -0.17   0.869    -.2277192     .192287
              |
combined_ineq#|
   c.ideology |
           1  |   .2014603   .0974993     2.07   0.039     .0103652    .3925554
              |
   experiment#|
combined_ineq#|
   c.ideology |
      TANF#1  |  -.1359098   .1709495    -0.80   0.427    -.4709646     .199145
--------------+----------------------------------------------------------------
        /cut1 |  -3.800015   .2488886                     -4.287828   -3.312202
        /cut2 |  -2.614636   .2364366                     -3.078043   -2.151229
        /cut3 |  -1.078244   .2302967                     -1.529618   -.6268712
        /cut4 |   .2867752   .2329646                     -.1698269    .7433774
-------------------------------------------------------------------------------

. eststo H4p_ideo

. ologit support i.experiment##i.combined_ineq##c.ideology $control, vce(cluster
>  caseid)

Iteration 0:  Log pseudolikelihood = -2741.0791  
Iteration 1:  Log pseudolikelihood = -2657.3839  
Iteration 2:  Log pseudolikelihood = -2656.7849  
Iteration 3:  Log pseudolikelihood = -2656.7846  
Iteration 4:  Log pseudolikelihood = -2656.7846  

Ordered logistic regression                             Number of obs =  1,803
                                                        Wald chi2(13) = 143.42
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -2656.7846                       Pseudo R2     = 0.0308

                                (Std. err. adjusted for 904 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
      support | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
   experiment |
        TANF  |  -.3964409   .3568822    -1.11   0.267    -1.095917    .3030353
1.combined_~q |  -.7353734   .3280671    -2.24   0.025    -1.378373   -.0923738
              |
   experiment#|
combined_ineq |
      TANF#1  |   .6195199   .5780344     1.07   0.284    -.5134066    1.752446
              |
     ideology |  -.2394621   .0911183    -2.63   0.009    -.4180508   -.0608735
              |
   experiment#|
   c.ideology |
        TANF  |   .0089636   .1130271     0.08   0.937    -.2125654    .2304927
              |
combined_ineq#|
   c.ideology |
           1  |   .2118634   .1040979     2.04   0.042     .0078354    .4158915
              |
   experiment#|
combined_ineq#|
   c.ideology |
      TANF#1  |  -.1913707   .1818235    -1.05   0.293    -.5477382    .1649967
              |
   pid7_clean |  -.1208363   .0366968    -3.29   0.001    -.1927608   -.0489118
  age_ordinal |  -.0954058   .0288773    -3.30   0.001    -.1520044   -.0388073
        White |   -.147191    .122014    -1.21   0.228     -.386334     .091952
         educ |  -.0182456   .0371945    -0.49   0.624    -.0911454    .0546542
       female |  -.0557101   .1045092    -0.53   0.594    -.2605442    .1491241
 faminc_clean |  -.0491206   .0163979    -3.00   0.003    -.0812599   -.0169812
--------------+----------------------------------------------------------------
        /cut1 |  -4.679465   .3428093                     -5.351359   -4.007571
        /cut2 |  -3.426636   .3284715                     -4.070428   -2.782844
        /cut3 |  -1.923095    .325635                     -2.561328   -1.284862
        /cut4 |  -.5432088   .3312555                     -1.192458    .1060401
-------------------------------------------------------------------------------

. eststo H4p_ideo_cont

. 
. esttab H4p_ideo H4p_ideo_cont using Tables/H4p_ideo.txt, tab se(2) b(2) pr2 on
> ecell label  nobaselevels nomtitles eqlabels(none) interaction(" X ")  scalars
> (N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" cut3 "Cutpoint 3" cut4 "Cut
> point 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/H4p_ideo.txt)

. 
. **figure D.2.
. ologit support i.experiment##i.combined_ineq##c.ideology $control, vce(cluster
>  caseid)

Iteration 0:  Log pseudolikelihood = -2741.0791  
Iteration 1:  Log pseudolikelihood = -2657.3839  
Iteration 2:  Log pseudolikelihood = -2656.7849  
Iteration 3:  Log pseudolikelihood = -2656.7846  
Iteration 4:  Log pseudolikelihood = -2656.7846  

Ordered logistic regression                             Number of obs =  1,803
                                                        Wald chi2(13) = 143.42
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -2656.7846                       Pseudo R2     = 0.0308

                                (Std. err. adjusted for 904 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
      support | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
   experiment |
        TANF  |  -.3964409   .3568822    -1.11   0.267    -1.095917    .3030353
1.combined_~q |  -.7353734   .3280671    -2.24   0.025    -1.378373   -.0923738
              |
   experiment#|
combined_ineq |
      TANF#1  |   .6195199   .5780344     1.07   0.284    -.5134066    1.752446
              |
     ideology |  -.2394621   .0911183    -2.63   0.009    -.4180508   -.0608735
              |
   experiment#|
   c.ideology |
        TANF  |   .0089636   .1130271     0.08   0.937    -.2125654    .2304927
              |
combined_ineq#|
   c.ideology |
           1  |   .2118634   .1040979     2.04   0.042     .0078354    .4158915
              |
   experiment#|
combined_ineq#|
   c.ideology |
      TANF#1  |  -.1913707   .1818235    -1.05   0.293    -.5477382    .1649967
              |
   pid7_clean |  -.1208363   .0366968    -3.29   0.001    -.1927608   -.0489118
  age_ordinal |  -.0954058   .0288773    -3.30   0.001    -.1520044   -.0388073
        White |   -.147191    .122014    -1.21   0.228     -.386334     .091952
         educ |  -.0182456   .0371945    -0.49   0.624    -.0911454    .0546542
       female |  -.0557101   .1045092    -0.53   0.594    -.2605442    .1491241
 faminc_clean |  -.0491206   .0163979    -3.00   0.003    -.0812599   -.0169812
--------------+----------------------------------------------------------------
        /cut1 |  -4.679465   .3428093                     -5.351359   -4.007571
        /cut2 |  -3.426636   .3284715                     -4.070428   -2.782844
        /cut3 |  -1.923095    .325635                     -2.561328   -1.284862
        /cut4 |  -.5432088   .3312555                     -1.192458    .1060401
-------------------------------------------------------------------------------

. margins, predict(outcome(5)) dydx(combined_ineq) over(experiment) at(ideology=
> (1(1)5))

Average marginal effects                                 Number of obs = 1,803
Model VCE: Robust

Expression: Pr(support==5), predict(outcome(5))
dy/dx wrt:  1.combined_ineq
Over:       experiment
1._at: 0.experiment
           ideology = 1
       1.experiment
           ideology = 1
2._at: 0.experiment
           ideology = 2
       1.experiment
           ideology = 2
3._at: 0.experiment
           ideology = 3
       1.experiment
           ideology = 3
4._at: 0.experiment
           ideology = 4
       1.experiment
           ideology = 4
5._at: 0.experiment
           ideology = 5
       1.experiment
           ideology = 5

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
0.combined_~q |  (base outcome)
--------------+----------------------------------------------------------------
1.combined_~q |
          _at#|
   experiment |
      1#EITC  |  -.0912337   .0422617    -2.16   0.031    -.1740652   -.0084022
      1#TANF  |  -.0151561   .0421245    -0.36   0.719    -.0977187    .0674065
      2#EITC  |  -.0506703   .0256243    -1.98   0.048     -.100893   -.0004475
      2#TANF  |  -.0104359   .0239272    -0.44   0.663    -.0573323    .0364606
      3#EITC  |  -.0150359   .0181954    -0.83   0.409    -.0506982    .0206263
      3#TANF  |  -.0065561   .0149026    -0.44   0.660    -.0357646    .0226523
      4#EITC  |   .0155667   .0228456     0.68   0.496    -.0292099    .0603433
      4#TANF  |  -.0034911   .0171146    -0.20   0.838     -.037035    .0300528
      5#EITC  |   .0412894   .0319476     1.29   0.196    -.0213267    .1039055
      5#TANF  |  -.0011666   .0224339    -0.05   0.959    -.0451363    .0428031
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, title("") ytitle("Inadequacy treatment effect on" "probability of
>  strong support") xtitle("") recast(line) recastci(rarea) ci1opts(fcolor(%30))
>  ci2opts(fcolor(%30)) xlabel(, angle(45)) legend(ring(0) pos(4) col(1)) yline(
> 0, lpattern(dot) lcolor(gs8))

Variables that uniquely identify margins: ideology experiment

. gr export Figures/h4p_ideo.png, as(png) replace
file Figures/h4p_ideo.png saved as PNG format

. 
. ****Appendix E. Ordered Logistic Regression Results and Figures (Party ID as t
> he Independent Variable of Interest)****
. //test H1&H2
. use CCES.dta, clear
(CCES 2020 module data cleaned for Qi & Haselswerdt policy info experiment, 2021
> -)

. global control "ideology age_ordinal White educ female faminc_clean"

. 
. **table E.1. (eitc_ineq and party ID)
. ologit eitc_support i.eitc_ineq##c.pid7_clean 

Iteration 0:  Log likelihood = -1484.4054  
Iteration 1:  Log likelihood = -1462.0085  
Iteration 2:  Log likelihood =  -1461.934  
Iteration 3:  Log likelihood =  -1461.934  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(3)    =  44.94
                                                        Prob > chi2   = 0.0000
Log likelihood = -1461.934                              Pseudo R2     = 0.0151

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.4961362   .2214416    -2.24   0.025    -.9301538   -.0621185
   pid7_clean |  -.2290161    .038083    -6.01   0.000    -.3036573   -.1543749
              |
    eitc_ineq#|
 c.pid7_clean |
EITC inade..  |   .1226557    .052329     2.34   0.019     .0200928    .2252187
--------------+----------------------------------------------------------------
        /cut1 |   -3.35204   .2015996                     -3.747168   -2.956912
        /cut2 |  -2.261955   .1792722                     -2.613322   -1.910588
        /cut3 |  -.6682756   .1653068                     -.9922709   -.3442802
        /cut4 |   .7502943   .1681529                      .4207207    1.079868
-------------------------------------------------------------------------------

. eststo eitc_pid

. ologit eitc_support i.eitc_ineq##c.pid7_clean $control

Iteration 0:  Log likelihood = -1344.4926  
Iteration 1:  Log likelihood = -1315.7492  
Iteration 2:  Log likelihood = -1315.6109  
Iteration 3:  Log likelihood = -1315.6108  

Ordered logistic regression                             Number of obs =    902
                                                        LR chi2(9)    =  57.76
                                                        Prob > chi2   = 0.0000
Log likelihood = -1315.6108                             Pseudo R2     = 0.0215

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.5681405   .2341827    -2.43   0.015     -1.02713   -.1091509
   pid7_clean |  -.1572622   .0483822    -3.25   0.001    -.2520895   -.0624349
              |
    eitc_ineq#|
 c.pid7_clean |
EITC inade..  |   .1271394   .0554671     2.29   0.022     .0184259     .235853
              |
     ideology |  -.1911875    .073924    -2.59   0.010    -.3360758   -.0462992
  age_ordinal |  -.0447931   .0353014    -1.27   0.204    -.1139825    .0243963
        White |  -.1420511   .1415086    -1.00   0.315    -.4194028    .1353007
         educ |  -.0500395   .0448736    -1.12   0.265    -.1379901    .0379112
       female |  -.0252088   .1236021    -0.20   0.838    -.2674645    .2170469
 faminc_clean |  -.0359573   .0197316    -1.82   0.068    -.0746306    .0027159
--------------+----------------------------------------------------------------
        /cut1 |  -4.435331   .3547293                     -5.130587   -3.740074
        /cut2 |  -3.309304   .3361456                     -3.968137    -2.65047
        /cut3 |  -1.761124   .3236402                     -2.395447   -1.126801
        /cut4 |  -.3150293    .322108                     -.9463494    .3162909
-------------------------------------------------------------------------------

. eststo eitc_pid_cont

. 
. esttab eitc_pid eitc_pid_cont using Tables/eitc_pid.txt, tab se(2) b(2) pr2 on
> ecell label  nobaselevels nomtitles eqlabels(none) interaction(" X ") scalars(
> N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" cut3 "Cutpoint 3" cut4 "Cutp
> oint 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/eitc_pid.txt)

. 
. margins, predict(outcome(5)) over(eitc_ineq) at(pid7_clean=(1(1)7))

Predictive margins                                         Number of obs = 902
Model VCE: OIM

Expression: Pr(eitc_support==5), predict(outcome(5))
Over:       eitc_ineq
1._at: 0.eitc_ineq
           pid7_clean = 1
       1.eitc_ineq
           pid7_clean = 1
2._at: 0.eitc_ineq
           pid7_clean = 2
       1.eitc_ineq
           pid7_clean = 2
3._at: 0.eitc_ineq
           pid7_clean = 3
       1.eitc_ineq
           pid7_clean = 3
4._at: 0.eitc_ineq
           pid7_clean = 4
       1.eitc_ineq
           pid7_clean = 4
5._at: 0.eitc_ineq
           pid7_clean = 5
       1.eitc_ineq
           pid7_clean = 5
6._at: 0.eitc_ineq
           pid7_clean = 6
       1.eitc_ineq
           pid7_clean = 6
7._at: 0.eitc_ineq
           pid7_clean = 7
       1.eitc_ineq
           pid7_clean = 7

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
_at#eitc_ineq |
           1 #|
EITC fraud..  |   .2536304    .030049     8.44   0.000     .1947354    .3125255
           1 #|
EITC inade..  |   .1869539   .0228478     8.18   0.000     .1421731    .2317347
           2 #|
EITC fraud..  |   .2254712   .0220848    10.21   0.000     .1821857    .2687566
           2 #|
EITC inade..  |   .1824829   .0181256    10.07   0.000     .1469574    .2180083
           3 #|
EITC fraud..  |   .1995701   .0170558    11.70   0.000     .1661413    .2329988
           3 #|
EITC inade..  |   .1780942   .0155563    11.45   0.000     .1476044     .208584
           4 #|
EITC fraud..  |    .175939   .0155075    11.35   0.000     .1455448    .2063332
           4 #|
EITC inade..  |   .1737875    .015858    10.96   0.000     .1427063    .2048687
           5 #|
EITC fraud..  |   .1545398   .0165974     9.31   0.000     .1220094    .1870702
           5 #|
EITC inade..  |   .1695624   .0186158     9.11   0.000     .1330761    .2060487
           6 #|
EITC fraud..  |   .1352934   .0186853     7.24   0.000     .0986709    .1719159
           6 #|
EITC inade..  |   .1654184   .0227442     7.27   0.000     .1208407    .2099961
           7 #|
EITC fraud..  |     .11809   .0207188     5.70   0.000      .077482    .1586981
           7 #|
EITC inade..  |   .1613549   .0274651     5.87   0.000     .1075243    .2151856
-------------------------------------------------------------------------------

. marginsplot, title("EITC experiment") ytitle("Predicted probability of strong 
> support") xtitle("") legend(ring(0) pos(7) col(1)) xlabel(, angle(45) labsize(
> small))ylabel(0(.1).4) recast(line) recastci(rarea) ci1opts(fcolor(%30)) ci2op
> ts(fcolor(%30)) saving(Figures/eitc_pid, replace)

Variables that uniquely identify margins: pid7_clean eitc_ineq
file Figures/eitc_pid.gph saved

. 
. **table E.2. (tanf_ineq and party ID)
. ologit tanf_support i.tanf_ineq##c.pid7_clean 

Iteration 0:  Log likelihood = -1525.7774  
Iteration 1:  Log likelihood =  -1491.262  
Iteration 2:  Log likelihood = -1491.0824  
Iteration 3:  Log likelihood = -1491.0824  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(3)    =  69.39
                                                        Prob > chi2   = 0.0000
Log likelihood = -1491.0824                             Pseudo R2     = 0.0227

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |  -.1511314   .2233657    -0.68   0.499    -.5889201    .2866574
   pid7_clean |  -.2379446   .0377489    -6.30   0.000     -.311931   -.1639582
              |
    tanf_ineq#|
 c.pid7_clean |
TANF inade..  |   .0342329   .0526693     0.65   0.516    -.0689971    .1374629
--------------+----------------------------------------------------------------
        /cut1 |  -3.140443   .1877334                     -3.508394   -2.772492
        /cut2 |   -1.86632   .1660549                     -2.191782   -1.540859
        /cut3 |  -.3668576   .1547114                     -.6700864   -.0636288
        /cut4 |    .938868   .1607498                      .6238042    1.253932
-------------------------------------------------------------------------------

. eststo tanf_pid

. ologit tanf_support i.tanf_ineq##c.pid7_clean  $control

Iteration 0:  Log likelihood = -1386.0474  
Iteration 1:  Log likelihood = -1334.9591  
Iteration 2:  Log likelihood = -1334.5308  
Iteration 3:  Log likelihood = -1334.5304  
Iteration 4:  Log likelihood = -1334.5304  

Ordered logistic regression                             Number of obs =    901
                                                        LR chi2(9)    = 103.03
                                                        Prob > chi2   = 0.0000
Log likelihood = -1334.5304                             Pseudo R2     = 0.0372

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |  -.1320955   .2362862    -0.56   0.576     -.595208    .3310169
   pid7_clean |   -.164697   .0481825    -3.42   0.001     -.259133    -.070261
              |
    tanf_ineq#|
 c.pid7_clean |
TANF inade..  |   .0229816   .0558929     0.41   0.681    -.0865664    .1325297
              |
     ideology |  -.1545266   .0754969    -2.05   0.041    -.3024979   -.0065554
  age_ordinal |  -.1497932   .0356956    -4.20   0.000    -.2197553   -.0798311
        White |  -.1504916   .1416383    -1.06   0.288    -.4280975    .1271143
         educ |   .0078976   .0445585     0.18   0.859    -.0794355    .0952306
       female |  -.0807682   .1230671    -0.66   0.512    -.3219753     .160439
 faminc_clean |  -.0594733   .0198112    -3.00   0.003    -.0983026   -.0206441
--------------+----------------------------------------------------------------
        /cut1 |  -4.454862   .3343764                     -5.110228   -3.799497
        /cut2 |  -3.093102   .3155392                     -3.711547   -2.474656
        /cut3 |  -1.618064   .3036451                     -2.213197    -1.02293
        /cut4 |  -.3101289   .3023927                     -.9028078      .28255
-------------------------------------------------------------------------------

. eststo tanf_pid_cont

. 
. esttab tanf_pid tanf_pid_cont using Tables/tanf_pid.txt, tab se(2) b(2) pr2 on
> ecell label  nobaselevels nomtitles eqlabels(none) interaction(" X ")  scalars
> (N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" cut3 "Cutpoint 3" cut4 "Cut
> point 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/tanf_pid.txt)

. 
. margins, predict(outcome(5)) over(tanf_ineq) at(pid7_clean=(1(1)7))

Predictive margins                                         Number of obs = 901
Model VCE: OIM

Expression: Pr(tanf_support==5), predict(outcome(5))
Over:       tanf_ineq
1._at: 0.tanf_ineq
           pid7_clean = 1
       1.tanf_ineq
           pid7_clean = 1
2._at: 0.tanf_ineq
           pid7_clean = 2
       1.tanf_ineq
           pid7_clean = 2
3._at: 0.tanf_ineq
           pid7_clean = 3
       1.tanf_ineq
           pid7_clean = 3
4._at: 0.tanf_ineq
           pid7_clean = 4
       1.tanf_ineq
           pid7_clean = 4
5._at: 0.tanf_ineq
           pid7_clean = 5
       1.tanf_ineq
           pid7_clean = 5
6._at: 0.tanf_ineq
           pid7_clean = 6
       1.tanf_ineq
           pid7_clean = 6
7._at: 0.tanf_ineq
           pid7_clean = 7
       1.tanf_ineq
           pid7_clean = 7

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
_at#tanf_ineq |
           1 #|
TANF fraud..  |   .2230255   .0255109     8.74   0.000     .1730251     .273026
           1 #|
TANF inade..  |     .19769   .0260963     7.58   0.000     .1465422    .2488379
           2 #|
TANF fraud..  |   .1965604   .0189052    10.40   0.000     .1595068    .2336139
           2 #|
TANF inade..  |    .176674   .0193078     9.15   0.000     .1388314    .2145166
           3 #|
TANF fraud..  |   .1724778   .0152562    11.31   0.000     .1425763    .2023793
           3 #|
TANF inade..  |   .1574163   .0149977    10.50   0.000     .1280213    .1868113
           4 #|
TANF fraud..  |   .1507382   .0146032    10.32   0.000     .1221165      .17936
           4 #|
TANF inade..  |   .1398679   .0135146    10.35   0.000     .1133798    .1663561
           5 #|
TANF fraud..  |   .1312575   .0157784     8.32   0.000     .1003324    .1621825
           5 #|
TANF inade..  |   .1239591   .0142275     8.71   0.000     .0960737    .1518445
           6 #|
TANF fraud..  |   .1139171   .0174472     6.53   0.000     .0797213    .1481129
           6 #|
TANF inade..  |   .1096045   .0158761     6.90   0.000      .078488     .140721
           7 #|
TANF fraud..  |   .0985751   .0189001     5.22   0.000     .0615315    .1356187
           7 #|
TANF inade..  |   .0967079   .0175914     5.50   0.000     .0622295    .1311863
-------------------------------------------------------------------------------

. marginsplot, title("TANF experiment") ytitle("Predicted probability of strong 
> support") xtitle("") legend(ring(0) pos(7) col(1)) xscale(r(0.75 5.25)) xlabel
> (, angle(45) labsize(small))ylabel(0(.1).4) recast(line) recastci(rarea) ci1op
> ts(fcolor(%30)) ci2opts(fcolor(%30)) saving(Figures/tanf_pid, replace)

Variables that uniquely identify margins: pid7_clean tanf_ineq
file Figures/tanf_pid.gph saved

. 
. **figure E.1.
. gr combine Figures/eitc_pid.gph Figures/tanf_pid.gph

. gr export Figures/pid_linear.png, as(png) replace
file Figures/pid_linear.png saved as PNG format

. 
. **table E.3.(eitc_ineq, party ID, and political knowledge)
. ologit eitc_support i.eitc_ineq##c.pid7_clean##c.polknow 

Iteration 0:  Log likelihood = -1484.4054  
Iteration 1:  Log likelihood = -1455.5342  
Iteration 2:  Log likelihood =  -1455.403  
Iteration 3:  Log likelihood =  -1455.403  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(7)    =  58.00
                                                        Prob > chi2   = 0.0000
Log likelihood = -1455.403                              Pseudo R2     = 0.0195

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.7775039   .5477578    -1.42   0.156    -1.851089    .2960816
   pid7_clean |  -.0378129   .0966477    -0.39   0.696    -.2272389     .151613
              |
    eitc_ineq#|
 c.pid7_clean |
EITC inade..  |   .1464365   .1308112     1.12   0.263    -.1099486    .4028217
              |
      polknow |   .1004654   .0655868     1.53   0.126    -.0280824    .2290131
              |
    eitc_ineq#|
    c.polknow |
EITC inade..  |   .0542275   .0878499     0.62   0.537    -.1179552    .2264102
              |
 c.pid7_clean#|
    c.polknow |   -.033438   .0153805    -2.17   0.030    -.0635832   -.0032929
              |
    eitc_ineq#|
 c.pid7_clean#|
    c.polknow |
EITC inade..  |  -.0050201   .0210348    -0.24   0.811    -.0462474    .0362073
--------------+----------------------------------------------------------------
        /cut1 |   -2.78345   .4253599                      -3.61714    -1.94976
        /cut2 |   -1.68416   .4160311                     -2.499566   -.8687537
        /cut3 |  -.0790119   .4122656                     -.8870376    .7290139
        /cut4 |   1.349494   .4157527                      .5346335    2.164354
-------------------------------------------------------------------------------

. eststo eitc_pid_polknow

. ologit eitc_support i.eitc_ineq##c.pid7_clean##c.polknow $control

Iteration 0:  Log likelihood = -1344.4926  
Iteration 1:  Log likelihood = -1309.2322  
Iteration 2:  Log likelihood = -1309.0071  
Iteration 3:  Log likelihood =  -1309.007  

Ordered logistic regression                             Number of obs =    902
                                                        LR chi2(13)   =  70.97
                                                        Prob > chi2   = 0.0000
Log likelihood = -1309.007                              Pseudo R2     = 0.0264

-------------------------------------------------------------------------------
 eitc_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    eitc_ineq |
EITC inade..  |  -.7644533   .5828979    -1.31   0.190    -1.906912    .3780056
   pid7_clean |   .0296478   .1054111     0.28   0.779    -.1769542    .2362497
              |
    eitc_ineq#|
 c.pid7_clean |
EITC inade..  |   .1385476   .1376905     1.01   0.314    -.1313208     .408416
              |
      polknow |   .1547132   .0744851     2.08   0.038      .008725    .3007014
              |
    eitc_ineq#|
    c.polknow |
EITC inade..  |   .0407124   .0930539     0.44   0.662    -.1416699    .2230947
              |
 c.pid7_clean#|
    c.polknow |  -.0352553   .0167497    -2.10   0.035     -.068084   -.0024266
              |
    eitc_ineq#|
 c.pid7_clean#|
    c.polknow |
EITC inade..  |  -.0031095    .022147    -0.14   0.888    -.0465168    .0402977
              |
     ideology |  -.1375077   .0757759    -1.81   0.070    -.2860256    .0110102
  age_ordinal |  -.0620056    .037949    -1.63   0.102    -.1363842     .012373
        White |  -.2120581   .1433113    -1.48   0.139     -.492943    .0688269
         educ |  -.0743041   .0471596    -1.58   0.115    -.1667352     .018127
       female |   .0297057   .1263319     0.24   0.814    -.2179004    .2773118
 faminc_clean |  -.0425689   .0203618    -2.09   0.037    -.0824773   -.0026606
--------------+----------------------------------------------------------------
        /cut1 |   -3.67018   .5457003                     -4.739733   -2.600628
        /cut2 |  -2.535131   .5356273                     -3.584941   -1.485321
        /cut3 |   -.974228   .5297727                     -2.012563    .0641074
        /cut4 |   .4855322   .5307128                     -.5546458     1.52571
-------------------------------------------------------------------------------

. eststo etic_pid_polknow_cont

. 
. esttab eitc_pid_polknow etic_pid_polknow_cont using Tables/eitc_pid_polknow.tx
> t, tab se(2) b(2) pr2 onecell label  nobaselevels nomtitles eqlabels(none) int
> eraction(" X ")  scalars(N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" cut
> 3 "Cutpoint 3" cut4 "Cutpoint 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/eitc_pid_polknow.txt)

. 
. margins, predict(outcome(5)) dydx(eitc_ineq) at(pid7_clean=(1(1)7) polknow=(0 
> 8))

Average marginal effects                                   Number of obs = 902
Model VCE: OIM

Expression: Pr(eitc_support==5), predict(outcome(5))
dy/dx wrt:  1.eitc_ineq
1._at:  pid7_clean = 1
        polknow    = 0
2._at:  pid7_clean = 1
        polknow    = 8
3._at:  pid7_clean = 2
        polknow    = 0
4._at:  pid7_clean = 2
        polknow    = 8
5._at:  pid7_clean = 3
        polknow    = 0
6._at:  pid7_clean = 3
        polknow    = 8
7._at:  pid7_clean = 4
        polknow    = 0
8._at:  pid7_clean = 4
        polknow    = 8
9._at:  pid7_clean = 5
        polknow    = 0
10._at: pid7_clean = 5
        polknow    = 8
11._at: pid7_clean = 6
        polknow    = 0
12._at: pid7_clean = 6
        polknow    = 8
13._at: pid7_clean = 7
        polknow    = 0
14._at: pid7_clean = 7
        polknow    = 8

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.eitc_ineq  |  (base outcome)
-------------+----------------------------------------------------------------
1.eitc_ineq  |
         _at |
          1  |  -.0647075   .0518838    -1.25   0.212    -.1663978    .0369828
          2  |  -.0646314   .0509676    -1.27   0.205    -.1645261    .0352633
          3  |  -.0540496   .0421248    -1.28   0.199    -.1366127    .0285136
          4  |  -.0385107   .0373413    -1.03   0.302    -.1116984    .0346769
          5  |  -.0414461    .035025    -1.18   0.237    -.1100937    .0272016
          6  |   -.016082    .028454    -0.57   0.572    -.0718509    .0396868
          7  |  -.0267243   .0344505    -0.78   0.438    -.0942461    .0407974
          8  |   .0023379   .0252062     0.09   0.926    -.0470654    .0517412
          9  |  -.0097307    .042973    -0.23   0.821    -.0939563    .0744948
         10  |   .0167719   .0261924     0.64   0.522    -.0345643     .068108
         11  |   .0096578   .0588069     0.16   0.870    -.1056016    .1249172
         12  |   .0274969   .0287804     0.96   0.339    -.0289118    .0839055
         13  |   .0315208   .0795655     0.40   0.692    -.1244247    .1874664
         14  |   .0349507   .0313431     1.12   0.265    -.0264806     .096382
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, title("EITC") ytitle("Inadequacy treatment effect on" "probabilit
> y of strong support") xtitle("") xscale(r(.75 5.25)) xlabel(, angle(45)) recas
> t(line) recastci(rarea) ci1opts(fcolor(%30)) ci2opts(fcolor(%30)) legend(order
> (3 "Low knowledge" 4 "High knowledge")) yline(0, lpattern(dot) lcolor(gs8)) sa
> ving(Figures/eitc_pidpk, replace)

Variables that uniquely identify margins: pid7_clean polknow
file Figures/eitc_pidpk.gph saved

. 
. **table E.4.(tanf_ineq, party ID, and political knowledge)
. ologit tanf_support i.tanf_ineq##c.pid7_clean##c.polknow 

Iteration 0:  Log likelihood = -1525.7774  
Iteration 1:  Log likelihood = -1483.3809  
Iteration 2:  Log likelihood = -1483.0828  
Iteration 3:  Log likelihood = -1483.0827  
Iteration 4:  Log likelihood = -1483.0827  

Ordered logistic regression                             Number of obs =    997
                                                        LR chi2(7)    =  85.39
                                                        Prob > chi2   = 0.0000
Log likelihood = -1483.0827                             Pseudo R2     = 0.0280

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |   .1278957   .5664062     0.23   0.821    -.9822401    1.238031
   pid7_clean |  -.0670697   .0920312    -0.73   0.466    -.2474475     .113308
              |
    tanf_ineq#|
 c.pid7_clean |
TANF inade..  |  -.0374753   .1343954    -0.28   0.780    -.3008854    .2259347
              |
      polknow |    .047765   .0605251     0.79   0.430     -.070862    .1663921
              |
    tanf_ineq#|
    c.polknow |
TANF inade..  |  -.0500396   .0902968    -0.55   0.579    -.2270181    .1269389
              |
 c.pid7_clean#|
    c.polknow |   -.031087   .0148991    -2.09   0.037    -.0602887   -.0018852
              |
    tanf_ineq#|
 c.pid7_clean#|
    c.polknow |
TANF inade..  |   .0133014   .0214669     0.62   0.536    -.0287729    .0553757
--------------+----------------------------------------------------------------
        /cut1 |   -2.89161   .3892445                     -3.654515   -2.128705
        /cut2 |  -1.594912   .3799912                     -2.339681   -.8501429
        /cut3 |  -.0774194   .3763199                     -.8149928     .660154
        /cut4 |   1.231101   .3797935                      .4867194    1.975482
-------------------------------------------------------------------------------

. eststo tanf_pid_polknow

. ologit tanf_support i.tanf_ineq##c.pid7_clean##c.polknow $control

Iteration 0:  Log likelihood = -1386.0474  
Iteration 1:  Log likelihood = -1332.7678  
Iteration 2:  Log likelihood = -1332.2694  
Iteration 3:  Log likelihood = -1332.2689  
Iteration 4:  Log likelihood = -1332.2689  

Ordered logistic regression                             Number of obs =    901
                                                        LR chi2(13)   = 107.56
                                                        Prob > chi2   = 0.0000
Log likelihood = -1332.2689                             Pseudo R2     = 0.0388

-------------------------------------------------------------------------------
 tanf_support | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    tanf_ineq |
TANF inade..  |   .1367455   .6068954     0.23   0.822    -1.052748    1.326239
   pid7_clean |  -.0316204   .0972554    -0.33   0.745    -.2222374    .1589967
              |
    tanf_ineq#|
 c.pid7_clean |
TANF inade..  |  -.0227503     .14263    -0.16   0.873       -.3023    .2567994
              |
      polknow |    .080795   .0658279     1.23   0.220    -.0482253    .2098153
              |
    tanf_ineq#|
    c.polknow |
TANF inade..  |  -.0498279   .0962599    -0.52   0.605    -.2384938     .138838
              |
 c.pid7_clean#|
    c.polknow |  -.0254596   .0156576    -1.63   0.104     -.056148    .0052288
              |
    tanf_ineq#|
 c.pid7_clean#|
    c.polknow |
TANF inade..  |   .0086566   .0227918     0.38   0.704    -.0360145    .0533277
              |
     ideology |  -.1289906   .0775024    -1.66   0.096    -.2808924    .0229112
  age_ordinal |  -.1362445   .0385955    -3.53   0.000    -.2118904   -.0605987
        White |  -.1661787   .1428267    -1.16   0.245    -.4461138    .1137565
         educ |   .0163647   .0465896     0.35   0.725    -.0749493    .1076787
       female |  -.0868489   .1253952    -0.69   0.489    -.3326189    .1589211
 faminc_clean |  -.0558488   .0203421    -2.75   0.006    -.0957187   -.0159789
--------------+----------------------------------------------------------------
        /cut1 |  -3.879678    .479915                     -4.820294   -2.939062
        /cut2 |  -2.507115   .4694041                      -3.42713     -1.5871
        /cut3 |  -1.025323   .4637259                     -1.934209   -.1164373
        /cut4 |   .2821882   .4639002                     -.6270394    1.191416
-------------------------------------------------------------------------------

. eststo tanf_pid_polknow_cont

. 
. esttab tanf_pid_polknow tanf_pid_polknow_cont using Tables/tanf_pid_polknow.tx
> t, tab se(2) b(2) pr2 onecell label  nobaselevels nomtitles eqlabels(none) int
> eraction(" X ")  scalars(N) substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" cut
> 3 "Cutpoint 3" cut4 "Cutpoint 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/tanf_pid_polknow.txt)

. 
. margins, predict(outcome(5)) dydx(tanf_ineq) at(pid7_clean=(1(1)7) polknow=(0 
> 8))

Average marginal effects                                   Number of obs = 901
Model VCE: OIM

Expression: Pr(tanf_support==5), predict(outcome(5))
dy/dx wrt:  1.tanf_ineq
1._at:  pid7_clean = 1
        polknow    = 0
2._at:  pid7_clean = 1
        polknow    = 8
3._at:  pid7_clean = 2
        polknow    = 0
4._at:  pid7_clean = 2
        polknow    = 8
5._at:  pid7_clean = 3
        polknow    = 0
6._at:  pid7_clean = 3
        polknow    = 8
7._at:  pid7_clean = 4
        polknow    = 0
8._at:  pid7_clean = 4
        polknow    = 8
9._at:  pid7_clean = 5
        polknow    = 0
10._at: pid7_clean = 5
        polknow    = 8
11._at: pid7_clean = 6
        polknow    = 0
12._at: pid7_clean = 6
        polknow    = 8
13._at: pid7_clean = 7
        polknow    = 0
14._at: pid7_clean = 7
        polknow    = 8

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.tanf_ineq  |  (base outcome)
-------------+----------------------------------------------------------------
1.tanf_ineq  |
         _at |
          1  |   .0164779   .0703603     0.23   0.815    -.1214257    .1543815
          2  |  -.0364821   .0431183    -0.85   0.398    -.1209924    .0480282
          3  |   .0128424   .0528743     0.24   0.808    -.0907893    .1164741
          4  |  -.0254026   .0305971    -0.83   0.406    -.0853717    .0345666
          5  |   .0093818   .0403441     0.23   0.816    -.0696911    .0884547
          6  |  -.0161235   .0226017    -0.71   0.476    -.0604219    .0281749
          7  |   .0060944   .0361456     0.17   0.866    -.0647496    .0769384
          8  |  -.0086499   .0193848    -0.45   0.655    -.0466434    .0293436
          9  |   .0029782    .041259     0.07   0.942    -.0778879    .0838444
         10  |  -.0028663   .0194661    -0.15   0.883    -.0410191    .0352865
         11  |   .0000307   .0519171     0.00   1.000    -.1017249    .1017862
         12  |    .001419   .0206224     0.07   0.945    -.0390002    .0418382
         13  |  -.0027512   .0646256    -0.04   0.966    -.1294151    .1239126
         14  |   .0044349   .0215738     0.21   0.837    -.0378489    .0467187
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, title("TANF") ytitle("Inadequacy treatment effect on" "probabilit
> y of strong support") xtitle("") xscale(r(.75 5.25)) xlabel(, angle(45)) recas
> t(line) recastci(rarea) ci1opts(fcolor(%30)) ci2opts(fcolor(%30)) legend(order
> (3 "Low knowledge" 4 "High knowledge")) yline(0, lpattern(dot) lcolor(gs8)) sa
> ving(Figures/tanf_pidpk, replace)

Variables that uniquely identify margins: pid7_clean polknow
file Figures/tanf_pidpk.gph saved

. 
. ****figure E.2. 
. grc1leg Figures/eitc_pidpk.gph Figures/tanf_pidpk.gph

. gr export Figures/pidpk.png, as(png) replace
file Figures/pidpk.png saved as PNG format

. 
. **table E.5. (tanf, combined inadequacy treatment, and party ID)
. use CCES_expanded.dta, clear
(CCES 2020 expanded module data for Qi & Haselswerdt policy info experiment, 202
> 1)

. global control "ideology age_ordinal White educ female faminc_clean"

. ologit support i.experiment##i.combined_ineq##c.pid7_clean, vce(cluster caseid
> ) 

Iteration 0:  Log pseudolikelihood =  -3019.633  
Iteration 1:  Log pseudolikelihood = -2955.1704  
Iteration 2:  Log pseudolikelihood = -2954.8507  
Iteration 3:  Log pseudolikelihood = -2954.8507  

Ordered logistic regression                             Number of obs =  1,994
                                                        Wald chi2(7)  = 111.61
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -2954.8507                       Pseudo R2     = 0.0215

                              (Std. err. adjusted for 1,000 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
      support | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
   experiment |
        TANF  |  -.2713428    .221736    -1.22   0.221    -.7059374    .1632518
1.combined_~q |  -.4888556   .2124273    -2.30   0.021    -.9052055   -.0725056
              |
   experiment#|
combined_ineq |
      TANF#1  |   .3338079   .3704583     0.90   0.368    -.3922771    1.059893
              |
   pid7_clean |  -.2253477    .038535    -5.85   0.000     -.300875   -.1498204
              |
   experiment#|
 c.pid7_clean |
        TANF  |  -.0162969   .0548859    -0.30   0.767    -.1238712    .0912775
              |
combined_ineq#|
 c.pid7_clean |
           1  |   .1213496   .0515267     2.36   0.019     .0203591    .2223401
              |
   experiment#|
combined_ineq#|
 c.pid7_clean |
      TANF#1  |  -.0868114   .0897009    -0.97   0.333    -.2626218    .0889991
--------------+----------------------------------------------------------------
        /cut1 |  -3.389059   .1813332                     -3.744466   -3.033652
        /cut2 |  -2.196287   .1656558                     -2.520966   -1.871607
        /cut3 |   -.651559   .1596219                     -.9644123   -.3387057
        /cut4 |   .7145775   .1647744                      .3916256    1.037529
-------------------------------------------------------------------------------

. eststo H4p_pid

. ologit support i.experiment##i.combined_ineq##c.pid7_clean $control, vce(clust
> er caseid)

Iteration 0:  Log pseudolikelihood = -2741.0791  
Iteration 1:  Log pseudolikelihood = -2655.9661  
Iteration 2:  Log pseudolikelihood = -2655.3486  
Iteration 3:  Log pseudolikelihood = -2655.3482  
Iteration 4:  Log pseudolikelihood = -2655.3482  

Ordered logistic regression                             Number of obs =  1,803
                                                        Wald chi2(13) = 148.24
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -2655.3482                       Pseudo R2     = 0.0313

                                (Std. err. adjusted for 904 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
      support | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
   experiment |
        TANF  |   -.327326   .2343109    -1.40   0.162     -.786567     .131915
1.combined_~q |  -.5696001   .2274018    -2.50   0.012    -1.015299   -.1239008
              |
   experiment#|
combined_ineq |
      TANF#1  |   .4220866   .3956028     1.07   0.286    -.3532806    1.197454
              |
   pid7_clean |  -.1551819   .0485899    -3.19   0.001    -.2504163   -.0599475
              |
   experiment#|
 c.pid7_clean |
        TANF  |  -.0126597    .057974    -0.22   0.827    -.1262867    .1009672
              |
combined_ineq#|
 c.pid7_clean |
           1  |   .1285272   .0549598     2.34   0.019      .020808    .2362464
              |
   experiment#|
combined_ineq#|
 c.pid7_clean |
      TANF#1  |  -.1018279   .0951335    -1.07   0.284    -.2882861    .0846304
              |
     ideology |  -.1733795   .0691801    -2.51   0.012      -.30897    -.037789
  age_ordinal |  -.0976644   .0290304    -3.36   0.001    -.1545629   -.0407658
        White |     -.1464   .1218948    -1.20   0.230    -.3853094    .0925093
         educ |  -.0207668   .0373313    -0.56   0.578    -.0939347    .0524012
       female |   -.051658   .1037064    -0.50   0.618    -.2549189    .1516028
 faminc_clean |  -.0476202   .0164171    -2.90   0.004    -.0797971   -.0154433
--------------+----------------------------------------------------------------
        /cut1 |  -4.614847    .304742                      -5.21213   -4.017563
        /cut2 |  -3.359435   .2903618                     -3.928534   -2.790337
        /cut3 |  -1.853988    .289094                     -2.420602   -1.287374
        /cut4 |   -.474397    .295609                      -1.05378     .104986
-------------------------------------------------------------------------------

. eststo H4p_pid_cont

. 
. esttab H4p_pid H4p_pid_cont using Tables/H4p_pid.txt, tab se(2) b(2) pr2 onece
> ll label  nobaselevels nomtitles eqlabels(none) interaction(" X ")  scalars(N)
>  substitute(cut1 "Cutpoint 1" cut2 "Cutpoint 2" cut3 "Cutpoint 3" cut4 "Cutpoi
> nt 4") star(* 0.10 ** 0.05 *** 0.01) replace
(output written to Tables/H4p_pid.txt)

. 
. **figure E.3.
. margins, predict(outcome(5)) dydx(combined_ineq) over(experiment) at(pid7_clea
> n=(1(1)7))

Average marginal effects                                 Number of obs = 1,803
Model VCE: Robust

Expression: Pr(support==5), predict(outcome(5))
dy/dx wrt:  1.combined_ineq
Over:       experiment
1._at: 0.experiment
           pid7_clean = 1
       1.experiment
           pid7_clean = 1
2._at: 0.experiment
           pid7_clean = 2
       1.experiment
           pid7_clean = 2
3._at: 0.experiment
           pid7_clean = 3
       1.experiment
           pid7_clean = 3
4._at: 0.experiment
           pid7_clean = 4
       1.experiment
           pid7_clean = 4
5._at: 0.experiment
           pid7_clean = 5
       1.experiment
           pid7_clean = 5
6._at: 0.experiment
           pid7_clean = 6
       1.experiment
           pid7_clean = 6
7._at: 0.experiment
           pid7_clean = 7
       1.experiment
           pid7_clean = 7

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
0.combined_~q |  (base outcome)
--------------+----------------------------------------------------------------
1.combined_~q |
          _at#|
   experiment |
      1#EITC  |  -.0759521   .0319068    -2.38   0.017    -.1384884   -.0134159
      1#TANF  |  -.0188397   .0309489    -0.61   0.543    -.0794984    .0418191
      2#EITC  |  -.0512827   .0241428    -2.12   0.034    -.0986016   -.0039637
      2#TANF  |  -.0133696   .0222866    -0.60   0.549    -.0570506    .0303114
      3#EITC  |  -.0286828   .0192982    -1.49   0.137    -.0665066    .0091411
      3#TANF  |  -.0086643   .0166256    -0.52   0.602    -.0412498    .0239212
      4#EITC  |   -.008194   .0181917    -0.45   0.652    -.0438492    .0274611
      4#TANF  |  -.0047046   .0144864    -0.32   0.745    -.0330974    .0236883
      5#EITC  |   .0101923   .0202682     0.50   0.615    -.0295326    .0499172
      5#TANF  |  -.0014478   .0151573    -0.10   0.924    -.0311556      .02826
      6#EITC  |   .0265276   .0239879     1.11   0.269    -.0204879     .073543
      6#TANF  |    .001165   .0169792     0.07   0.945    -.0321135    .0344436
      7#EITC  |   .0408969   .0282766     1.45   0.148    -.0145241    .0963179
      7#TANF  |   .0032028   .0188304     0.17   0.865    -.0337042    .0401098
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, title("") ytitle("Inadequacy treatment effect on" "probability of
>  strong support") xtitle("") recast(line) recastci(rarea) ci1opts(fcolor(%30))
>  ci2opts(fcolor(%30)) xlabel(, angle(45)) legend(ring(0) pos(4) col(1)) yline(
> 0, lpattern(dot) lcolor(gs8))

Variables that uniquely identify margins: pid7_clean experiment

. gr export Figures/h4p_pid.png, as(png) replace
file Figures/h4p_pid.png saved as PNG format

. 
. log close 
      name:  <unnamed>
       log:  E:\PHD\Research\EITC public opinion\EITC HQ JH\Data\Replication dat
> a\CCES_analyses.log
  log type:  text
 closed on:  25 Sep 2023, 12:33:18
--------------------------------------------------------------------------------
